Supply Chain Network Digital Twin

Challenges

Data Silos: traditionally Isolated data within organizations can lead to fragmented views of the supply chain, resulting in misalignment across teams and functions.

Complexity in building out an end-to-end digital twin: Traditional supply chain modeling can be time-consuming, often requiring 8 to 10 weeks for creating an as-is baseline model, hindering timely decision-making.

Lack of Real-Time Update: Without real-time update, digital twins can’t respond to changes proactively, leading to risks and inefficiencies.

Sophus Solution

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AI-powered data integration: Sophus consolidates data across the end to end supply chain, offering a unified view and data model that enhances decision-making.

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Real-Time Digital Twin: By having a digital replica of the supply chain network, Sophus provides real-time monitoring and predictive analytics, enabling proactive management of the network.

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Rapid Baselining Feature: Sophus’s Rapid Baselining reduces the baseline-building process from 8 weeks to just a few days or hours, expediting the effort of modeling a supply chain digital twin.

Benefits

Reduced time to answer: fast and easy way to create the supply chain network digital twin model, and reduce time to answer by 3-5X

Real-Time Scenario Modeling: A digital twin provides a real-time view of the entire supply chain network, and potentially enables businesses to simulate “what-if” scenarios in a proactive manner

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